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Karen Feigh is an Associate Professor in the Daniel Guggenheim School of Aerospace Engineering. She holds a B.S. in Aerospace Engineering from Georgia Tech, a MPhil in Aeronautics from Cranfield University, UK, and a Ph.D. in Industrial and Systems Engineering from Georgia Tech. Karen has previously worked on fast-time air traffic simulation, conducted ethnographic studies of airline and fractional ownership operation control centers, and designed expert systems for air traffic control towers. Her doctoral work was conducted at Georgia Tech's Cognitive Engineering Center where she used cognitive engineering methods to improve support system design methods to more closely match the dynamic needs of airline operations managers to aid with recovery from irregular operations. Her awards include the Marshall scholarship and the AIAA Orville and Wilbur Wright Graduate award.

Dr. Feigh's research interests include:

Decision Support System Design

How to design support systems for naturalistic decision making often found in aviation domains?

How to design control algorithms to explicitly account for human limitations and actively bound and manipulate human workload?

Computational Cognitive Modeling for Engineering Design

How to incorporate cognitive models into the engineering design process?

How to model human cognition at a level of abstraction appropriate for engineering design?

How to advance theories of cognitive engineering into the realm of computation such that descriptive models can be transformed into prescriptive ones?

She is active in the design of cognitive work support systems for individuals and teams in dynamic socio-technical settings, including airline operations, air transportation systems, UAV and MAV ground control stations, mission control centers, and command and control centers.

Recent Projects

NASA’s future missions will push the bounds of human-space exploration and challenge the mission designers and engineers to create automated systems that will enable the joint human-automation teams to operate more autonomously as they move further from terrestrially based mission control and the time lag of communication becomes a challenge.

Future manned space missions will require astronauts to work with a variety of robotic systems. To develop effective human-robot teams, NASA needs objective methods for function allocation between humans and robots. This study develops an objective methodology for function allocation between humans and robots for future manned space missions. Some problems that need to be addressed in function allocation include: (a) monitoring of agents, (b) agents waiting on other agents (idle time), (c) high task load of agents, (d) excessive amount of communication required.

The main objective of this research is to use techniques and models from human factors, computational neuroscience, and adaptive and real-time optimal control theory in order to investigate the effects of the introduction of learning and adaptation to the next generation of ASCS. In particular, we will:
(a) Learn the driver’s habits, driving skills, patterns and weaknesses.
(b) Model his/her current cognitive state along multiple dimensions such as attentiveness, aggressiveness, etc.

The research aims to contribute to analyze human/automation roles and responsibilities. This work will provide scenario-based methods for validation and verification of current day and NextGen concepts of operation and automated forms supporting these concepts of operation.

Human spaceflight is arguably one of mankind's most challenging engineering feats, requiring carefully crafted synergy between human and technological capabilities. One critical component of human spaceflight pertains to the activity conducted outside the safe confines of the spacecraft, known as Extravehicular Activity (EVA). Successful execution of EVAs requires significant effort and real-time communication between astronauts who perform the EVA and the ground personnel who provide real-time support.